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NAVIGATING FINANCIAL SERVICES IN 2021: LOW-CODE TO THE RESCUE

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Nick Ford, Chief Technology Evangelist, Mendix

 

Financial services are the poster child of great digital transformation: today, Britons can pay from their watches, check their balance directly from their phone at any time and even automate trading. This level of innovation isn’t only about customers: traders are able to operate faster than ever before thanks to better predictive analysis and forecasting tools, and finance teams are able to collaborate from anywhere in the world.

While we embrace all this innovation, it’s easy to forget that the reality of the sector is incredibly complex. The radical changes induced by COVID-19 have highlighted how challenging maintaining innovation today really is, while putting more pressure on IT teams to accelerate the digital transformation of the sector even further.

On top of this, the sector is one of the most affected by Brexit. Mendix’s Navigating the UK Landscape research found that businesses in the financial services sector have serious concerns about the impact of Brexit on their industry. Many believe that Brexit has damaged the reputation of the UK as a centre of finance (67%) – as well as creating functional challenges for businesses in the country.

Many financial services organisations are turning to technology, and specifically low-code, to deal with these challenges. This piece will look at how firms in the sector can use low-code to navigate the new world.

 

A sea of challenges

Financial services are complex: there are thousands of products to choose from, from savings to investment and mortgages. These services are then managed by lots of different companies, creating an additional level of complexity: banks, fintechs, brokers, wealth management specialists, government bodies… the list goes on. To add yet another layer, there’s a network of regulations, which change over time, forcing IT leaders to constantly keep on top of the latest evolution in the sector. Knowing these is only the first step: every time new laws are implemented, the sector needs to adjust to them, and that can mean anything from revising security protocols to radically changing the way information is processed, transmitted or audited.

This may already look complicated, but the real complexity starts underneath, in the realms of processes that the IT manages to keep the company operating as normal. It would be fair to say that the mission of financial IT leaders is often underrated: they deal with antiquated systems dating back decades, inadequate data management processes and minute security and compliance considerations every day, simply to keep the business afloat. Add to this the need to get all staff to work remotely during the lockdown, and the already time-poor IT leaders are now completely swamped.

Brexit also makes things difficult for financial services organisations. Two thirds anticipate costly and complicated processes for crossborder payments and investments, while 59% believe it will be harder to attract foreign investments. Ultimately, 61% admit they will no longer be able to support some of their customers because of the transition.

 

Tech as a raft

While the sector is mired down with complex processes and inadequate tools, it also needs to deal with a major challenge: fierce competition for tech-savvy customers. Now, all banks, investment firms and wealth management companies are investing in tech to help them cope with new customer demands for easier access to their capital and increased transparency. Two thirds have deployed digital projects to make the business more flexible as a result of Brexit, with data management (62%) and digital processes (62%) particular focal points.

And this is not just about pleasing digitally minded customers: it’s also about improving productivity and operational efficiency, harnessing data, and solving compliance challenges. This balancing act between priorities is gathering pace and spreading across the business: today, IT teams must deliver innovation that’s fast, reliable and secure, and that supports many divisions — all at once. It’s a big challenge, but it’s one that IT leaders are willing to tackle head on: two thirds of IT leaders believe the value of digital transformation initiatives outweighs their inherent risk. Yet, IT leaders know that rushing would be a mistake: although IT teams face high demand for their support, most would not prioritise speed over caution, even if they could innovate faster. This measured pace ensures that financial organisations are delivering the right solutions at the right time, reducing the risk of service disruption and security challenges.

 

Low-code to the rescue

To manage all these priorities, the IT team needs to look beyond its own team to create revenue-generating services that truly answer the clients’ needs – and it needs to empower all developers with the right tools to do so. This improves collaboration between IT and customer-facing staff to design services that suit the needs of the customer base, while reducing the pressure of an already-stretched IT team. Enter low-code: most leaders (58%) say that low-code has enabled the development of new applications to support their companies post-Brexit.

One example of this is a Financial Institution, which perceived its digital user experience lacking and engaged low-code to install a new user experience for its portal, consumer and wholesale digital services. It was able to do this in just eight months, providing numerous benefits to stakeholders.

Low-code software development provides a simple solution to address these constraints and challenges: based on a visual approach for building applications using drag-and-drop components, it enables non-technical staff to participate in creating business applications, even if they have little to no coding experience. Working separately or in close collaboration, professional developers and business-side “citizen developers” can create, iterate, and release applications in a fraction of the time it takes with traditional methods, all under the watchful governance of IT to ensure their applications comply with enterprise standards and architecture.

A low-code approach allows for flexible, iterative app development for many use cases in the financial services sector, including legacy application upgrades to comply with new regulations, apps supporting smart banking or portfolio management, and mortgage application management. With low-code, the financial services industry has the right tools to untangle its complex processes, simplify its evolution and focus on its core mission: keeping the economy thriving.

 

Finance

AIRBANK SELECTS YAPILY TO BUILD A FINANCIAL MANAGEMENT SOLUTION FOR SMBS

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Airbank, a financial management solution for European startups and SMBs, has selected open banking infrastructure provider Yapily to help its users manage their finances with ease.

Airbank provides a simple financial management solution that aggregates all bank accounts in one place and delivers more control, visibility, and automation to modern finance teams. Startups & SMBs use Airbank to access bank accounts, monitor cash flow in real-time, create reliable forecasts, and make business payments.

Airbank matches bank transactions with merchant and category data to give finance teams complete visibility into revenues and expenses, thus helping make their lives easier with cash flow budgeting, forecasting, and reporting.

Yapily’s API infrastructure provides Airbank users with a smooth, simple way to connect to more than 1,500 banks across the UK and Europe including Deutsche Bank, Commerzbank, Sparkassen, Volksbanken and neobanks. Airbank selected Yapily for its strong coverage in Europe, with a specific focus on Germany, France, Spain, and the UK. Yapily’s European bank connectivity enables Airbank’s customers to scale and grow across Europe, delivering forecast visibility anywhere they go.

The partnership with Yapily alleviates Airbank’s customers from spending time and resources managing their finances – giving them direct access to all the financial and contextual data they need in one tool. Historically, most businesses created budgets and cash flow forecasts in manual spreadsheets which is time-consuming and error-prone. With Airbank, customers save time and costs to focus on value-adding business tasks.

The partnership also enables Airbank’s customers to use its data enrichment platform and transaction categorisation engine to turn the raw data from bank accounts into meaningful and actionable insights. Airbank reconciles account balances, forecasts financials and helps business owners make smarter business decisions every day. Harnessing Yapily’s leading open banking infrastructure, Airbank can accelerate its adoption of digital banking services.

Airbank’s vision is to simplify financial management for SMBs and to create a unified platform that helps its users with the full cycle of financial management from cash flow analysis and forecasting, to accounts receivables and payables management, and more. Airbank has raised $3m seed funding from leading VCs, and counts hundreds of users in Germany, Austria, France, Spain and the UK.

Open Banking has enabled smooth integrations with banks, which we utilize to offer richer banking and payments experiences for our users. We’re building a business banking solution that connects all your financial accounts in one place. Our partnership with Yapily gives users a smooth and simple way to connect to thousands of banks in Europe, unlocking real-time insights into their cash flow. We eliminate the pains of finance admin so business owners can focus on what’s really important — growing their business.

Christopher Zemina, Co-founder and CEO of Airbank

Airbank helps simplify the daily routine of banking and finance management for small and medium sized businesses. By leveraging Yapily’s open banking infrastructure, Airbank can provide actionable insights to businesses – at a time where it’s needed. As a small yet fast growing company, Yapily is committed to supporting the SMB community and we are excited to see how Airbank delivers the benefits of open banking to many businesses across Europe.

Comment by Chris Scheuermann, Commercial Lead DACH at Yapily

 

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AI AND HOW IT’S LEADING THE FIGHT AGAINST FRAUD IN THE FINANCIAL SECTOR

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Geoff Clark, Managing Director, Aerospike EMEA

Much like many other sectors financial institutions have accelerated their digital transformation projects since the beginning of the pandemic. Lockdown meant that customers could no longer visit local branches or meet in person with their financial advisor. Financial institutions have no choice but to find alternative ways to serve their customers.

We saw banks quickly adapt and improve their automation tools to interact with their customers online.  Technologies that enable chatbots, credit card brokerage, contactless payment cards, digital verification for onboarding, online insurance applications, mobile apps, recommendation engines, robo-investing and robotic process automation (RPA) were just some of the many solutions deployed. Here in Europe, Ernst and Young (E&Y) reported an increase of 72% increase in the use of FinTech apps since the start of COVID-19.

Geoff Clark

Cybercriminals typically opt for the lowest hanging fruit and as financial institutions clambered to expand their digital services the cybercriminals looked to identify and exploit any weakness in the infrastructure providing the backbone for these technologies. Exploiting the vulnerabilities of financial institutions is not new as they have long been a coveted target for fraudsters. In the main, that’s due to the wealth of sensitive personal and financial information they hold. Throw into the mix pandemic relief funds, increased unemployment benefits, and stimulus payments, and you have the perfect playground for fraudsters.

A recent report found that every dollar lost to fraud costs financial service companies as much as $3.78 — an increase from $3.25 in 2019. But fraud’s impact is much deeper than financial loss. It drains company resources to investigate and prosecute fraud, damages reputations, and puts customer retention at risk. For these reasons alone, it is imperative that the appropriate systems and processes are in place to combat fraud.

 

Analysing Fraud

The majority of financial institutions still rely on dated rule-based systems to mitigate fraud risk. These systems can consist of thousands of predefined rules that store, sort, and manipulate data to find fraud patterns. For example, a rule could say, if there is a credit card transaction in one state and another transaction in a different state within a 30-minute time frame, then this is likely a fraudulent transaction and therefore it declines the transaction.

Rule-based systems are static, hard-coded, and time-consuming to update, and are often one step behind the sophisticated techniques fraudsters use. When fraud occurs, the typical response is to create another rule that prevents another attack, but it’s often too late.

Fraudsters continue to find new ways to commit fraud that rules don’t capture.

The trend we’re seeing from financial institutions is to replace rule-based systems with AI and machine learning-based systems as they’re more effective. These systems are largely self-learning and there is so much more data available and the more information they’re fed the more effective they can be. Rather than using tens of data attributes with rule-based systems, AI and machine learning-based systems can analyse hundreds of data attributes over enormous data sets and longer time frames to automatically detect with higher accuracy unusual behaviours that indicate fraud. For example, Barclays Bank has implemented AI systems to detect and mitigate fraud improving the customer experience in the process through the reduction of false positives and false negatives.

AI and machine learning-based systems are heading toward explainable AI (XAI), an emerging sector in machine learning that addresses how AI systems arrive at their black-box decisions. Financial institutions know the inputs and outputs of these systems, but they lack visibility into how they reached the results.

Building XAI into AI systems enables banks to understand how decisions are made and create better models to improve their systems by removing bias. For example, suppose a fraud system declines a legitimate customer’s credit card transaction. In this situation the financial institution needs to understand why the false positive has occurred so it can further refine its model.

XAI also has data privacy in its favour particularly when it comes to compliance. Under the European Union’s General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA)—and with other data privacy laws coming—financial institutions need to comply with specific mandates. They must be able to explain how they use a customer’s personal information and how they came to decision such as declining a credit card transaction. Overlaying XAI on top of their AI systems, ensures they have far great visibility into how decisions are being made by AI/ML systems.

 

Constructing a Fraud System Architecture

To emulate some of the industry’s more innovative organisations financial institutions must understand and pursue best practices when building their AI-based fraud systems. They should work alongside technology organisations but also work with their line of business managers to understand how fraud is impacting their business, what their greatest weaknesses are, how customer satisfaction can be improved, and how they can incorporate customer fraud/risk metrics into their customer analytics to improve their omnichannel marketing campaigns. Customer data collected and analysed by fraud teams are some of the most robust depositories of customer information making them invaluable to marketers.

When looking to build a world-class system, financial services firms should consider the following steps:

  • The fraud system needs to likely consume hundreds of terabytes of data, perhaps even petabytes for the largest firms.
  • Data must be continuously updated in real time from many sources such as internal customer and transaction data from storefronts, web pages, and mobile devices, as well as third-party demographic, behavioural, geo-location, identity management, credit bureau, and other data types.
  • This data will usually need to be prepared, e.g., cleansed, standardised, and normalised, to convert it into a form that AI/ML models can more easily digest and understand.
  • The data needs to move back to the central data platform to be further enriched.
  • At this point those financial institutions can fine-tune the model parameters, test and select the optimal machine learning algorithms, feed them with data to learn the underlying patterns, and validate the model’s accuracy to make good decisions using data that was not part of the training set.

After the above steps are completed and they are satisfied the model can be deployed to act in the microsecond moments that are necessary to fight fraud.

As technology evolves at such a fast pace all organisations must aim to implement a fraud solution that can combat the increasingly sophisticated fraudsters while implementing the following key elements

  1. Large data sets (TeraBytes, PetaBytes) consisting of both internal company data supplemented with third-party data;
  2. Highly optimised and validated AI/ML algorithms that detect fraud and minimise false positives and false negatives;
  3. A real-time data platform capable of running these AI/ML algorithms across enormous data sets in sub-millisecond response times to provide customers with the fast customer experience that they expect.

 

 

 

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